Control software system

Tao, Heyi

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https://hdl.handle.net/2142/124128 Copy

Description

Title

Control software system

Author(s)

Tao, Heyi

Issue Date

2024-02-02

Director of Research (if dissertation) or Advisor (if thesis)

Hoiem, Derek W

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

Computer Vision, Artificial Intelligence, Machine Learning, Natural Language Processing

Abstract

A primary objective in AI development is to empower AI agents to effectively interact with their environments and proficiently perform various tasks with good generalization ability. Web-based computer software programs serve as excellent examples due to their versatility, encompassing a wide range of tasks, each with numerous variations. Ideally, designed system should be able to understand how to correctly perform those tasks based on their interfaces as well as natural language descriptions given pre-defined actions without having large amount of demonstration examples or training data. Previous approaches, such as reinforcement learning (RL) or imitation learning (IL), are inefficient to train and often task-specific. In this paper, we explores the development of a system that leverages large language models (LLMs) to automatically execute web-based software tasks through actions such as clicking, entering text, and scrolling. As the observation to the software interfaces, we utilize the Document Object Model (DOM) elements. Our system only generates and executes one action at a time based on the current observations of the interface. We also provide either one human manually annotated example, or an automatically generated example based on a successful zero-shot trial by letting LLM self-exploring the correct programs to correctly solve the tasks. Our designed system has been evaluated on the MiniWob++ benchmark. By having only one in-context learning example, our system achieves comparable performance than other methods that have been used many training data or demonstrations and trials.

Graduation Semester

2024-05

Type of Resource

Thesis

Copyright and License Information

Copyright 2023 Heyi Tao

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